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An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength
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This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperature exerted the most significant influence at 100%, while sample dimensions had a minimal impact at 17.9%. In addition, the mathematical model closest to the proposed was the Bazli model, because the latter depends on two variables (thickness and temperature). The ANN accurately predicted the residual tensile strength of GFRP at elevated temperatures, achieving a correlation coefficient of 97.3% and a determination coefficient of 94.3%.

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Publication Date
Sun Feb 01 2015
Journal Name
Journal Of Engineering
Punching Shear Strength of Self Compacted Ferrocement Slabs
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This study aims to investigate the behavior and strength of self-compacted ferrocement slabs under punching shear load. Experimental results of thirteen square ferrocement slabs of 500×500 mm simply supported on all edges are presented.  The main parameters investigated include the volume fraction of reinforcement, slab thickness and size of load-bearing plate. The load deflection and cracking characteristics of the tested slabs are studied and compared.  The test results showed that the volume fraction of wire mesh has significant effect on both ultimate load and displacement. The increase of slab thickness leads to decrease in deflection values and increase in stiffness of slabs. Both ductility and stiffness increase as the

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Publication Date
Tue Dec 01 2009
Journal Name
Iraqi Journal Of Physics
Oscillator Strength and Quantum Efficiency of Fluoranthene Molecule
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The fluorescence and absorption spectra of Fluoranthene dissolved in
cyclohexane and ethanol were studied and analyzed. The effect of the
concentration of this molecule and the polarity of the solvents on the spectral
shifts and on relative intensity has been investigated. A computational program
was written in order to convert the spectra from grapher to data. Some
photophysical parameters such as oscillator strength and quantum efficiency have
been calculated. Fluorescence quantum efficiency of Fluoranthene was measured
relative to Quinine Sulfate (QS) in 1N H2SO4. The obtained values were (0.5) in
cyclohexane and (0.45) in ethanol

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Publication Date
Sat Oct 31 2020
Journal Name
International Journal Of Integrated Engineering
Behavior of High Strength Hybrid Reinforcement Concrete Beams
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Six proposed simply supported high strength-steel fiber reinforced concrete (HS-SFRC) beams reinforced with FRP (fiber reinforced polymer) rebars were numerically tested by finite element method using ABAQUS software to investigate their behavior under the flexural failure. The beams were divided into two groups depending on their cross sectional shape. Group A consisted of four trapezoidal beams with dimensions of (height 200 mm, top width 250 mm, and bottom width 125 mm), while group B consisted of two rectangular beams with dimensions of (125 ×200) mm. All specimens have same total length of 1500 mm, and they were also considered to be made of same high strength concrete designed material with 1% volume fraction of steel fiber.

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Publication Date
Sun Oct 01 2017
Journal Name
Journal Of Engineering
Strength of Reinforced Concrete Columns with Transverse Openings
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The present work is concerned with the investigation of the behavior and ultimate capacity of axially loaded reinforced concrete columns in presence of transverse openings under axial load plus uniaxial bending. The experimental program includes testing of twenty reinforced concrete columns (150 × 150 × 700 mm) under concentric and eccentric load. Parameters considered include opening size, load eccentricity and influence of the direction of load eccentricity with respect to the longitudinal axis of the opening. Experimental results are discussed based on load – lateral mid height deflection curves, load – longitudinal shortening behavior, ultimate load and failure modes. It is found that when the direction of load

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Publication Date
Wed Feb 23 2022
Journal Name
Iraqi Journal Of Agricultural Sciences
AN ECONOMIC ANALYSIS OF SOME FACTORS AFFECTING IN MARKETING EFFICIENCY OF DRY ONION CROP USING THE TOBIT REGRESSION MODEL
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The study was aimed to evaluate the marketing efficiency of dry Onion crop in Salah al-Deen, as estimate the impact of some quality and quantity factors in the efficiency of marketing process of crop using Tobit regression model. The average marketing efficiency of the research sample was 71.3686%. The marketing margins differed according to the marketing channel followed in marketing the crop. The qualitative and quantitative variables in the model are productivity, family size, distance from the market, educational level. The estimated model revealed that a variable productivity is the most important and influential in marketing efficiency, followed by the variable of the distance between the farm and the market, then the variable

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Publication Date
Wed Feb 23 2022
Journal Name
Iraqi Journal Of Agricultural Sciences
AN ECONOMIC ANALYSIS OF SOME FACTORS AFFECTING IN MARKETING EFFICIENCY OF DRY ONION CROP USING THE TOBIT REGRESSION MODEL
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The study was aimed to evaluate the marketing efficiency of dry Onion crop in Salah al-Deen, as estimate the impact of some quality and quantity factors in the efficiency of marketing process of crop using Tobit regression model. The average marketing efficiency of the research sample was 71.3686%. The marketing margins differed according to the marketing channel followed in marketing the crop. The qualitative and quantitative variables in the model are productivity, family size, distance from the market, educational level. The estimated model revealed that a variable productivity is the most important and influential in marketing efficiency, followed by the variable of the distance between the farm and the market, then the variable

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Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset
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The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

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Publication Date
Sun Mar 01 2020
Journal Name
Journal Of Engineering
Wellbore Breakouts Prediction from Different Rock Failure Criteria
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One of the wellbore instability problems in vertical wells are breakouts in Zubair oilfield. Breakouts, if exceeds its critical limits will produce problems such as loss circulation which will add to the non-productive time (NPT) thus increasing loss in costs and in total revenues. In this paper, three of the available rock failure criteria (Mohr-Coulomb, Mogi-Coulomb and Modified-Lade) are used to study and predict the occurrence of the breakouts. It is found that there is an increase over the allowable breakout limit in breakout width in Tanuma shaly formation and it was predicted using Mohr-Coulomb criterion. An increase in the pore pressure was predicted in Tanuma shaly formation, thus; a new mud weight and casing pr

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Publication Date
Wed Sep 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Application
Suggested methods for prediction using semiparametric regression function
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Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

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Publication Date
Mon Sep 30 2013
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Permeability Prediction in Carbonate Reservoir Rock Using FZI
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Knowledge of permeability, which is the ability of rocks to transmit the fluid, is important for understanding the flow mechanisms in oil and gas reservoirs.
Permeability is best measured in the laboratory on cored rock taken from the reservoir. Coring is expensive and time-consuming in comparison to the electronic survey techniques most commonly used to gain information about permeability.
Yamama formation was chosen, to predict the permeability by using FZI method. Yamama Formation is the main lower cretaceous carbonate reservoir in southern of Iraq. This formation is made up mainly of limestone. Yamama formation was deposited on a gradually rising basin floor. The digenesis of Yamama sediments is very important due to its direct

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